class_docstr: A baseline approach for computing input attribution. It returns
  the gradients with respect to inputs. If `abs` is set to True, which is
  the default, the absolute value of the gradients is returned.
  
  More details about the approach can be found in the following paper:
      https://arxiv.org/abs/1312.6034
docstr: Args:
  
      forward_func (Callable): The forward function of the model or
                  any modification of it.
docstr_tok: ["Args",":","forward_func","(","Callable",")",":","The","forward","function","of","the","model","or","any","modification","of","it","."]
code: def __init__(self, forward_func: Callable) -> None:
      r"""
      Args:
  
          forward_func (Callable): The forward function of the model or
                      any modification of it.
      """
      GradientAttribution.__init__(self, forward_func)
  
file_path: captum/captum/attr/_core/saliency.py
imports: from typing import Any, Callable
  import torch
  from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
  from captum._utils.gradient import apply_gradient_requirements, undo_gradient_requirements
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.attribution import GradientAttribution
  from captum.log import log_usage
label_desc: Use image node_id 1 to create a new Saliency object from inherited base classes: GradientAttribution with example: obj = Saliency(forward_func)